LUX.ipynb

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Development</td>\n      <td>3</td>\n      <td>4</td>\n      <td>Life Sciences</td>\n      <td>1</td>\n      <td>5</td>\n      <td>4</td>\n      <td>Female</td>\n      <td>56</td>\n      <td>3</td>\n      <td>1</td>\n      <td>Research Scientist</td>\n      <td>3</td>\n      <td>Married</td>\n      <td>2909</td>\n      <td>23159</td>\n      <td>1</td>\n      <td>Y</td>\n      <td>Yes</td>\n      <td>11</td>\n      <td>3</td>\n      <td>3</td>\n      <td>80</td>\n      <td>0</td>\n      <td>8</td>\n      <td>3</td>\n      <td>3</td>\n      <td>8</td>\n      <td>7</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1.000000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>27</td>\n      <td>No</td>\n      <td>Travel_Rarely</td>\n      <td>591</td>\n      <td>Research &amp; Development</td>\n      <td>2</td>\n      <td>1</td>\n      <td>Medical</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>Male</td>\n      <td>40</td>\n      <td>3</td>\n      <td>1</td>\n      <td>Laboratory Technician</td>\n      <td>2</td>\n      <td>Married</td>\n      <td>3468</td>\n      <td>16632</td>\n      <td>9</td>\n      <td>Y</td>\n      <td>No</td>\n      <td>12</td>\n      <td>3</td>\n      <td>4</td>\n      <td>80</td>\n      <td>1</td>\n      <td>6</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.333333</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1465</th>\n      <td>36</td>\n      <td>No</td>\n      <td>Travel_Frequently</td>\n      <td>884</td>\n      <td>Research &amp; Development</td>\n      <td>23</td>\n      <td>2</td>\n      <td>Medical</td>\n      <td>1</td>\n      <td>2061</td>\n      <td>3</td>\n      <td>Male</td>\n      <td>41</td>\n      <td>4</td>\n      <td>2</td>\n      <td>Laboratory Technician</td>\n      <td>4</td>\n      <td>Married</td>\n      <td>2571</td>\n      <td>12290</td>\n      <td>4</td>\n      <td>Y</td>\n      <td>No</td>\n      <td>17</td>\n      <td>3</td>\n      <td>3</td>\n      <td>80</td>\n      <td>1</td>\n      <td>17</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0.294118</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1466</th>\n      <td>39</td>\n      <td>No</td>\n      <td>Travel_Rarely</td>\n      <td>613</td>\n      <td>Research &amp; Development</td>\n      <td>6</td>\n      <td>1</td>\n      <td>Medical</td>\n      <td>1</td>\n      <td>2062</td>\n      <td>4</td>\n      <td>Male</td>\n      <td>42</td>\n      <td>2</td>\n      <td>3</td>\n      <td>Healthcare Representative</td>\n      <td>1</td>\n      <td>Married</td>\n      <td>9991</td>\n      <td>21457</td>\n      <td>4</td>\n      <td>Y</td>\n      <td>No</td>\n      <td>15</td>\n      <td>3</td>\n      <td>1</td>\n      <td>80</td>\n      <td>1</td>\n      <td>9</td>\n      <td>5</td>\n      <td>3</td>\n      <td>7</td>\n      <td>7</td>\n      <td>1</td>\n      <td>7</td>\n      <td>0.777778</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1467</th>\n      <td>27</td>\n      <td>No</td>\n      <td>Travel_Rarely</td>\n      <td>155</td>\n      <td>Research &amp; Development</td>\n      <td>4</td>\n      <td>3</td>\n      <td>Life Sciences</td>\n      <td>1</td>\n      <td>2064</td>\n      <td>2</td>\n      <td>Male</td>\n      <td>87</td>\n      <td>4</td>\n      <td>2</td>\n      <td>Manufacturing Director</td>\n      <td>2</td>\n      <td>Married</td>\n      <td>6142</td>\n      <td>5174</td>\n      <td>1</td>\n      <td>Y</td>\n      <td>Yes</td>\n      <td>20</td>\n      <td>4</td>\n      <td>2</td>\n      <td>80</td>\n      <td>1</td>\n      <td>6</td>\n      <td>0</td>\n      <td>3</td>\n      <td>6</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1.000000</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1468</th>\n      <td>49</td>\n      <td>No</td>\n      <td>Travel_Frequently</td>\n      <td>1023</td>\n      <td>Sales</td>\n      <td>2</td>\n      <td>3</td>\n      <td>Medical</td>\n      <td>1</td>\n      <td>2065</td>\n      <td>4</td>\n      <td>Male</td>\n      <td>63</td>\n      <td>2</td>\n      <td>2</td>\n      <td>Sales Executive</td>\n      <td>2</td>\n      <td>Married</td>\n      <td>5390</td>\n      <td>13243</td>\n      <td>2</td>\n      <td>Y</td>\n      <td>No</td>\n      <td>14</td>\n      <td>3</td>\n      <td>4</td>\n      <td>80</td>\n      <td>0</td>\n      <td>17</td>\n      <td>3</td>\n      <td>2</td>\n      <td>9</td>\n      <td>6</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0.529412</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1469</th>\n      <td>34</td>\n      <td>No</td>\n      <td>Travel_Rarely</td>\n      <td>628</td>\n      <td>Research &amp; Development</td>\n      <td>8</td>\n      <td>3</td>\n      <td>Medical</td>\n      <td>1</td>\n      <td>2068</td>\n      <td>2</td>\n      <td>Male</td>\n      <td>82</td>\n      <td>4</td>\n      <td>2</td>\n      <td>Laboratory Technician</td>\n      <td>3</td>\n      <td>Married</td>\n      <td>4404</td>\n      <td>10228</td>\n      <td>2</td>\n      <td>Y</td>\n      <td>No</td>\n      <td>12</td>\n      <td>3</td>\n      <td>1</td>\n      <td>80</td>\n      <td>0</td>\n      <td>6</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0.666667</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>1470 rows × 37 columns</p>\n</div>",
                "text/plain": "      Age Attrition  ... %WorkingYearsAtCompany  IsOld\n0      41       Yes  ...               0.750000   True\n1      49        No  ...               1.000000   True\n2      37       Yes  ...               0.000000   True\n3      33        No  ...               1.000000  False\n4      27        No  ...               0.333333  False\n...   ...       ...  ...                    ...    ...\n1465   36        No  ...               0.294118  False\n1466   39        No  ...               0.777778   True\n1467   27        No  ...               1.000000  False\n1468   49        No  ...               0.529412   True\n1469   34        No  ...               0.666667  False\n\n[1470 rows x 37 columns]"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/swarnava-96/Exploratory-Data-Analysis-EDA-/blob/main/LUX.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tPez-DBa-Vl",
        "outputId": "332a374d-5244-4c68-dad0-90ff8d136c37"
      },
      "source": [
        "!pip install lux-api"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: lux-api in /usr/local/lib/python3.7/dist-packages (0.3.1)\n",
            "Requirement already satisfied: lux-widget>=0.1.4 in /usr/local/lib/python3.7/dist-packages (from lux-api) (0.1.6)\n",
            "Requirement already satisfied: scipy>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from lux-api) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.7/dist-packages (from lux-api) (1.19.5)\n",
            "Requirement already satisfied: iso3166 in /usr/local/lib/python3.7/dist-packages (from lux-api) (1.0.1)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from lux-api) (3.2.2)\n",
            "Requirement already satisfied: altair>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from lux-api) (4.1.0)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from lux-api) (0.22.2.post1)\n",
            "Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from lux-api) (1.2.5)\n",
            "Requirement already satisfied: ipywidgets>=7.5.0 in /usr/local/lib/python3.7/dist-packages (from lux-widget>=0.1.4->lux-api) (7.6.3)\n",
            "Requirement already satisfied: notebook>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from lux-widget>=0.1.4->lux-api) (5.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->lux-api) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->lux-api) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->lux-api) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->lux-api) (2.8.1)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.7/dist-packages (from altair>=4.0.0->lux-api) (0.11.1)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.7/dist-packages (from altair>=4.0.0->lux-api) (2.6.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from altair>=4.0.0->lux-api) (2.11.3)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.7/dist-packages (from altair>=4.0.0->lux-api) (0.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->lux-api) (1.0.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.2.0->lux-api) (2018.9)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (5.1.3)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (1.0.0)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (4.10.1)\n",
            "Requirement already satisfied: ipython>=4.0.0; python_version >= \"3.3\" in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (5.5.0)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (5.0.5)\n",
            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (3.5.1)\n",
            "Requirement already satisfied: tornado>=4 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (5.1.1)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.10.1)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (5.6.1)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (1.5.0)\n",
            "Requirement already satisfied: jupyter-core>=4.4.0 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (4.7.1)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.2.0)\n",
            "Requirement already satisfied: jupyter-client>=5.2.0 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (5.3.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=3.0.0->lux-api) (1.15.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->altair>=4.0.0->lux-api) (2.0.1)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (0.8.1)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (4.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (4.4.2)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (1.0.18)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (2.6.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (0.7.5)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (57.0.0)\n",
            "Requirement already satisfied: ptyprocess; os_name != \"nt\" in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.7.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (1.4.3)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.5.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.8.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.7.1)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (3.3.0)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client>=5.2.0->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (22.1.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.0->lux-widget>=0.1.4->lux-api) (0.2.5)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.0.0->lux-widget>=0.1.4->lux-api) (20.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oIAj-Oe4cEzJ"
      },
      "source": [
        "## Visualizations of dataframes beyond simple tables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CySQD5skbCMo"
      },
      "source": [
        "import pandas as pd\n",
        "import lux"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QuEmkbObQGq"
      },
      "source": [
        "# Collecting basic usage statistics for Lux (For more information, see: https://tinyurl.com/logging-consent)\n",
        "lux.logger = True # Remove this line if you do not want your interactions recorded"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tcR3bdVbYOY"
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/employee.csv\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641,
          "referenced_widgets": [
            "e9916974574c4d46941c706720073595",
            "8cbdebb2236543c998a1daea81f67e3c",
            "f2453699a9d24a73b3c4990a4d90101d",
            "ae8cbdd774164f7492b4a824d21f580e",
            "1096c15e14ef4ba88f3970f78a9c3928"
          ]
        },
        "id": "KJHZnwysbiOw",
        "outputId": "97fec60f-8735-43f1-899b-64e0cb6be069"
      },
      "source": [
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e9916974574c4d46941c706720073595",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Button(description='Toggle Pandas/Lux', layout=Layout(top='5px', width='140px'), style=ButtonStyle())"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae8cbdd774164f7492b4a824d21f580e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKtu5BFg0iW3"
      },
      "source": [
        "## Steering analysis with intent"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7R5yqnSW0jh0"
      },
      "source": [
        "df.intent=[\"Attrition\"]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641,
          "referenced_widgets": [
            "68816de7cd4d4f0da71a8dc800c176b6",
            "93225f37907f495294d3941194057cb4",
            "16362bb77b744a1c8deb1b1be1fdd3fc",
            "5f965d4026d14877b2ce894e5473e774",
            "cd46ad303b5e47f3bddf4ff50925b94f"
          ]
        },
        "id": "vIYY7vcm0pBM",
        "outputId": "719ddb01-5ee8-43f4-ce2a-2a6e4d6576ad"
      },
      "source": [
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "68816de7cd4d4f0da71a8dc800c176b6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Button(description='Toggle Pandas/Lux', layout=Layout(top='5px', width='140px'), style=ButtonStyle())"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f965d4026d14877b2ce894e5473e774",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jIzpVPC_1AFi"
      },
      "source": [
        "## Quick inspection of 1-D Series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
            "dac2d2e7ef0f442e9a54b591809d92ac",
            "0badfee281624d15b58f56d3528a7dd0",
            "2bfc25f96b77453e9eca0dc4eb519c53",
            "da355caddfed4321a03bbbc1302f1aa3",
            "ecc598ea7c6046b38bdf54e4d662df16"
          ]
        },
        "id": "jJA-kb4N1BfR",
        "outputId": "b62c9db0-70cc-4bb6-d576-da1af0d14b81"
      },
      "source": [
        "df[\"YearsAtCompany\"]/df[\"TotalWorkingYears\"]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dac2d2e7ef0f442e9a54b591809d92ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Button(description='Toggle Pandas/Lux', layout=Layout(top='5px', width='140px'), style=ButtonStyle())"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da355caddfed4321a03bbbc1302f1aa3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-39aM0oS1ES6"
      },
      "source": [
        "df[\"%WorkingYearsAtCompany\"]=df[\"YearsAtCompany\"]/df[\"TotalWorkingYears\"]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tD5aOwt1W_i"
      },
      "source": [
        "## Quick-and-dirty experimentation with visualizations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "acd4b312d1c14764afa9df9240dd44c5"
          ]
        },
        "id": "NDxjFUll1X6s",
        "outputId": "929e5717-7c2c-4a23-f443-0645a06c589f"
      },
      "source": [
        "from lux.vis.Vis import Vis\n",
        "Vis([\"%WorkingYearsAtCompany\",\"Age\"],df)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "acd4b312d1c14764afa9df9240dd44c5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "LuxWidget(current_vis={'config': {'view': {'continuousWidth': 400, 'continuousHeight': 300}, 'axis': {'labelCo…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
            "db527602bec742e7989098e815079e73",
            "9fe5b5c57d1d4fddb05d913dd6963701",
            "fadad1cd6d304aa2bb4d3bef48c547a9",
            "3001f048e96d4d5da590d16d326130c8",
            "39ac8381904245099966483799b5b19c"
          ]
        },
        "id": "GLEEiCgh1ajJ",
        "outputId": "fd350d92-c68f-42c5-e40f-29eeb977448b"
      },
      "source": [
        "df[\"Age\"]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db527602bec742e7989098e815079e73",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Button(description='Toggle Pandas/Lux', layout=Layout(top='5px', width='140px'), style=ButtonStyle())"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3001f048e96d4d5da590d16d326130c8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paqmjW791d_P"
      },
      "source": [
        "df[\"IsOld\"]=df[\"Age\"]>df[\"Age\"].mean()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
            "c4fc618684374200a7e84d546fd23ea4",
            "cdb69dc80dbe49c5b976d5978255472f",
            "cca4b35052e340b382db6113ed3c5bcd",
            "4bdaa3bb686940b79aee22247e723833",
            "e024b328014945ec801913d9205cbb3a"
          ]
        },
        "id": "BWr-dPMb1hHa",
        "outputId": "744274a2-64b5-4941-bb6b-9eea7085fc6b"
      },
      "source": [
        "df[\"IsOld\"]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4fc618684374200a7e84d546fd23ea4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Button(description='Toggle Pandas/Lux', layout=Layout(top='5px', width='140px'), style=ButtonStyle())"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4bdaa3bb686940b79aee22247e723833",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "c2401d653c244b3da4fd09beaa39fd4f"
          ]
        },
        "id": "v0q_zb4T1jyO",
        "outputId": "94bf34c7-8631-4d6c-be50-0a272d9a9ddf"
      },
      "source": [
        "Vis([\"%WorkingYearsAtCompany\",\"IsOld\"],df)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c2401d653c244b3da4fd09beaa39fd4f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "LuxWidget(current_vis={'config': {'view': {'continuousWidth': 400, 'continuousHeight': 300}, 'axis': {'labelCo…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UIR3uFyl1p8C",
        "outputId": "e1c92b7b-3edb-403e-9e4a-142762d0c58b"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
              "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
              "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
              "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
              "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
              "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
              "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
              "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
              "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
              "       'YearsWithCurrManager', '%WorkingYearsAtCompany', 'IsOld'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eN1FpCl1snW",
        "outputId": "650b7153-6c55-4245-c363-ab229928f6a6"
      },
      "source": [
        "compensation = list(filter(lambda col: 'Rate' in col, df.columns))\n",
        "compensation"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DailyRate', 'HourlyRate', 'MonthlyRate']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnAhfEf_1vgX"
      },
      "source": [
        "from lux.vis.VisList import VisList"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "f5acd4e295e540268a8eb31133039e76"
          ]
        },
        "id": "Q2YZBRkh1y0F",
        "outputId": "af1a471d-1659-4914-d526-089897abab86"
      },
      "source": [
        "VisList([compensation,\"IsOld\"],df)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5acd4e295e540268a8eb31133039e76",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "LuxWidget(recommendations=[{'action': 'Vis List', 'description': 'Shows a vis list defined by the intent', 'vs…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4H4m018U17OU"
      },
      "source": [
        "## Exporting visualization insight to edit and share"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641,
          "referenced_widgets": [
            "20f52d36d1454547b31f619f8e61c073",
            "974600bfe97c4c2b874972829e216f29",
            "f7337904ebaa4b1b9f092c62d806af13",
            "93066c2814954250a6b25da14c225636",
            "39494f14f7804d8cba0aaf0c49a20f2a"
          ]
        },
        "id": "unTMYe0_1-3j",
        "outputId": "d8f4f2dd-ba09-424e-cb91-bf64231f5b40"
      },
      "source": [
        "df"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "20f52d36d1454547b31f619f8e61c073",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Button(description='Toggle Pandas/Lux', layout=Layout(top='5px', width='140px'), style=ButtonStyle())"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "93066c2814954250a6b25da14c225636",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "id": "StzQB-kP2LFM",
        "outputId": "a4f93aa1-af1d-44a0-bacf-5d4af591636c"
      },
      "source": [
        "import altair as alt\n",
        "visData = pd.DataFrame({'Attrition': {0: 'No', 1: 'Yes'}, \n",
        "                        'DistanceFromHome': {0: 8.915652879156529, 1: 10.632911392405063}})\n",
        "\n",
        "chart = alt.Chart(visData,title=\"Important Factor for Employee Attrition!!\").mark_bar().encode(\n",
        "    y = alt.Y('Attrition', type= 'nominal', axis=alt.Axis(labelOverlap=True, title='Attrition')),\n",
        "    x = alt.X('DistanceFromHome', type= 'quantitative', axis=alt.Axis(title='Average Distance from Home')),\n",
        ")\n",
        "chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding'))\n",
        "chart = chart.configure_title(fontWeight=500,fontSize=13,font='Helvetica Neue')\n",
        "chart = chart.configure_axis(titleFontWeight=500,titleFontSize=11,titleFont='Helvetica Neue',\n",
        "            labelFontWeight=400,labelFontSize=8,labelFont='Helvetica Neue',labelColor='#505050')\n",
        "chart = chart.configure_legend(titleFontWeight=500,titleFontSize=10,titleFont='Helvetica Neue',\n",
        "            labelFontWeight=400,labelFontSize=8,labelFont='Helvetica Neue')\n",
        "chart = chart.properties(width=250,height=70)\n",
        "\n",
        "chart"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alt.Chart(...)"
            ],
            "text/html": [
              "\n",
              "<div id=\"altair-viz-e252a4d61185443798ba914f6e00483e\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "  (function(spec, embedOpt){\n",
              "    let outputDiv = document.currentScript.previousElementSibling;\n",
              "    if (outputDiv.id !== \"altair-viz-e252a4d61185443798ba914f6e00483e\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-e252a4d61185443798ba914f6e00483e\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
              "    };\n",
              "\n",
              "    function loadScript(lib) {\n",
              "      return new Promise(function(resolve, reject) {\n",
              "        var s = document.createElement('script');\n",
              "        s.src = paths[lib];\n",
              "        s.async = true;\n",
              "        s.onload = () => resolve(paths[lib]);\n",
              "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "      });\n",
              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else if (typeof vegaEmbed === \"function\") {\n",
              "      displayChart(vegaEmbed);\n",
              "    } else {\n",
              "      loadScript(\"vega\")\n",
              "        .then(() => loadScript(\"vega-lite\"))\n",
              "        .then(() => loadScript(\"vega-embed\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"labelColor\": \"#505050\", \"labelFont\": \"Helvetica Neue\", \"labelFontSize\": 8, \"labelFontWeight\": 400, \"titleFont\": \"Helvetica Neue\", \"titleFontSize\": 11, \"titleFontWeight\": 500}, \"legend\": {\"labelFont\": \"Helvetica Neue\", \"labelFontSize\": 8, \"labelFontWeight\": 400, \"titleFont\": \"Helvetica Neue\", \"titleFontSize\": 10, \"titleFontWeight\": 500}, \"mark\": {\"tooltip\": {\"content\": \"encoding\"}}, \"title\": {\"font\": \"Helvetica Neue\", \"fontSize\": 13, \"fontWeight\": 500}}, \"data\": {\"name\": \"data-bc11a90267224468760c7f3f4d553d31\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Average Distance from Home\"}, \"field\": \"DistanceFromHome\"}, \"y\": {\"type\": \"nominal\", \"axis\": {\"labelOverlap\": true, \"title\": \"Attrition\"}, \"field\": \"Attrition\"}}, \"height\": 70, \"title\": \"Important Factor for Employee Attrition!!\", \"width\": 250, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-bc11a90267224468760c7f3f4d553d31\": [{\"Attrition\": \"No\", \"DistanceFromHome\": 8.915652879156529}, {\"Attrition\": \"Yes\", \"DistanceFromHome\": 10.632911392405063}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    }
  ]
}